Member of Engineering (Human Data)

In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.

poolside exists to be this company - to build a world where AI will be the engine behind economically valuable work and scientific progress.

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We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year.

Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts.

As a Member of Engineering (Human Data), you will lead the development and management of high-quality data labeling pipelines that support our large language models. This role involves building an internal labeling team, working closely with vendors, and designing scalable processes for data annotation.

While the position does not include customer-facing responsibilities, your work will be critical to the success of our AI models, ensuring that they are trained on top-tier labeled data using crowdsourcing and other data collection techniques.

To build and optimize scalable data labeling pipelines that power the success of our machine learning models.

• Design, develop, and implement scalable data labeling pipelines that integrate into model training workflows. Manage and expand the internal data labeling team to meet the company's growing needs
• Collaborate with external vendors to source and manage crowdsourced data labeling efforts, ensuring timely and high-quality delivery
• Monitor and improve labeling processes by conducting experiments, ensuring data quality, and optimizing performance across labeling projects
• Set up metrics and QA processes to evaluate the quality of labeled data and continuously improve output
• Work cross-functionally with researchers and engineers to align labeling pipelines with model training needs
• Identify new tools and technologies to streamline labeling processes and increase efficiency

• Experience with designing and managing data labeling processes, with a strong emphasis on crowdsourcing solutions
• 2+ years of experience in a technical role such as Data Engineer, Data Scientist, Technical Project Manager, or similar, ideally in machine learning/data-focused environments
• Familiarity with managing vendors and crowdsourcing platforms to handle large-scale data labeling efforts
• Strong understanding of data quality metrics such as accuracy, precision, recall, and F1 score
• Proven ability to develop complex pipelines with multiple stages, particularly for data annotation and machine learning training
• Experience with cloud platforms and tools such as AWS, GCP, Kubernetes, and CI/CD systems is a plus
• Ability to collaborate with technical teams and ensure labeling processes align with overall model development needs
• Mandatory experience with crowdsourcing platforms (e.g., ScaleAI, Toloka, or similar) for data labeling
• Strong problem-solving skills and ability to work independently in a fast-paced environment

• Intro call with Eiso, our CTO & Co-Founder
• Technical Interview(s) with one of our Founding Engineers
• Team fit call with the People team
• Final interview with Eiso, our CTO & Co-Founder

• Fully remote work & flexible hours
• 37 days/year of vacation & holidays
• Health insurance allowance for you and dependents
• Company-provided equipment
• Wellbeing, always-be-learning and home office allowances
• Frequent team get togethers
• Great diverse & inclusive people-first culture